[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Thabet et al., 2019 - Google Patents

Sample-efficient deep reinforcement learning with imaginary rollouts for human-robot interaction

Thabet et al., 2019

View PDF
Document ID
13531545060384721578
Author
Thabet M
Patacchiola M
Cangelosi A
Publication year
Publication venue
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

External Links

Snippet

Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Furthermore, the unpredictability of human behavior in human-robot interaction tasks can …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems

Similar Documents

Publication Publication Date Title
Vecerik et al. Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Karkus et al. Differentiable algorithm networks for composable robot learning
Mandlekar et al. Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
Wang et al. Robust imitation of diverse behaviors
US20210390653A1 (en) Learning robotic tasks using one or more neural networks
Rahmatizadeh et al. Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration
Thabet et al. Sample-efficient deep reinforcement learning with imaginary rollouts for human-robot interaction
Ding et al. Challenges of reinforcement learning
Ren et al. Generalization guarantees for imitation learning
Cuccu et al. Intrinsically motivated neuroevolution for vision-based reinforcement learning
Hu et al. On Transforming Reinforcement Learning With Transformers: The Development Trajectory
CN113379027A (en) Method, system, storage medium and application for generating confrontation interactive simulation learning
Sakunthala et al. A review on artificial intelligence techniques in electrical drives: Neural networks, fuzzy logic, and genetic algorithm
Hafez et al. Efficient intrinsically motivated robotic grasping with learning-adaptive imagination in latent space
Tanwani Generative models for learning robot manipulation skills from humans
Seo et al. Continuous control with coarse-to-fine reinforcement learning
JP2021192141A (en) Learning device, learning method, and learning program
Dinerstein et al. Learning policies for embodied virtual agents through demonstration
Kobayashi et al. Latent representation in human–robot interaction with explicit consideration of periodic dynamics
Przystupa et al. Deep probabilistic movement primitives with a bayesian aggregator
Chien et al. Stochastic curiosity maximizing exploration
Lötzsch Using deep reinforcement learning for the continuous control of robotic arms
Li et al. A hierarchical reinforcement learning method for persistent time-sensitive tasks
Pong Goal-Directed Exploration and Skill Reuse
Chen et al. Imitating shortest paths for visual navigation with trajectory-aware deep reinforcement learning